Distributed representations of words as word embeddings have transformed the way that natural language processing tasks like IE can be tackled. Word2Vec [1]} and GloVe [2]} are two sets of large and publicly available word embeddings that are used for many NLP tasks. Most neural network-based models for information extraction have used the distributed representation of words as their core component. The high dimensional distributed representation of words can encode important semantic information about words, which is very helpful for identifying the relations among the entities present in a sentence. Initially, neural models also follow the pipeline approach to solve this task.
